Data science turns data into knowledge. More than numbers, models, or metrics, it seeks to extract meaning from complex information in order to guide decisions, solve problems, and expand our understanding of society, systems, and the behaviors that shape them.
Software engineering, in turn, transforms this knowledge into concrete projects: systems, applications, tools, and processes capable of making ideas functional, accessible, and useful for businesses, communities, and people.
This notebook brings together notes, studies, experiments, and reflections on data science, machine learning, software engineering, and applied research. It is a space for investigating how data, technology, and critical thinking can come together in the construction of practical, responsible solutions with real-world impact.
Artificial intelligence appears here as a tool, not as an automatic replacement for authorship. It is used critically and contextually at different stages of the process: sometimes as support for composition, revision, organization of ideas, or grammar adjustments; other times in a minimal, almost invisible way. In some specific cases, especially in literature reviews, a text may be produced entirely with AI support, always based on human reading, curation, guidance, and decision-making. The degree of AI participation varies according to each text, its intention, and its context. For this reason, all texts include a footnote indicating the method used in their preparation. Authorship remains a human gesture of choice, interpretation, responsibility, and meaning.
